Second, we did not perform a robustness study of the hyper-parameters related to data cleaning in Step 2. For the ABM [1]}, as we noted in §REF , McGuirl et al. [2]} showed that no cleaning is needed before computing persistent homology, so any reasonable hyper-parameter values (including removing no cells) in Step 2 works. For CA [3]} patterns, data cleaning is much more important, and selecting the values of hyper-parameters in Step 2 is a balance between (1) preserving the structure of CA patterns and (2) removing outlier cells that disrupt our interpretation of persistent homology. The choice of hyper-parameters that we made in §REF  allowed us to interpret topological summaries as information about stripes and interstripes in Step 3. Working with outliers when computing persistent homology is challenging in general, and it would be interesting to apply methods from statistical topological data analysis [4]}, [5]}, [6]}, [7]} to zebrafish patterns in the future.
